Adaptive Drone Monitoring for On-Site Crop Anomaly Detection
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Solution Overview
Problem
Current aerial monitoring systems for agriculture are costly, labor-intensive, and inefficient, requiring high-bandwidth connectivity and skilled personnel, and fail to provide actionable insights in a timely manner, leading to suboptimal field monitoring and reduced crop yields.
Innovation Solution
A cyber-physical system using adaptive multi-stage flight planning algorithms and computationally efficient image processing techniques allows for autonomous identification and classification of anomalous areas in agricultural fields, enabling on-site data analysis and feedback-driven improvement, reducing the need for cloud-based data upload and minimizing technical expertise required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If current aerial monitoring systems are used, then field monitoring coverage is improved, but cost and labor requirements increase significantly
Solution Approach 1:
The patent uses consumer-grade drones with standard cameras instead of expensive specialized aerial monitoring equipment. These inexpensive, readily available devices can be deployed frequently and discarded or replaced easily, eliminating the need for costly professional-grade sensors and reducing overall system investment while maintaining adequate monitoring capabilities
Solution Approach 2:
The system performs autonomous anomaly detection and classification without requiring skilled agronomists to manually analyze each image. The automated processing pipeline including color space transformation, anomaly detection algorithms, and classification models enables the system to self-analyze captured images and generate actionable insights, significantly reducing labor requirements
2Loss of information
If high-bandwidth cloud connectivity is used for data upload, then data analysis capability is improved, but operational cost and dependency increase
Solution Approach 1:
The patent transforms the data processing dimension from cloud-based to edge-based by implementing anomaly detection and classification algorithms directly on the drone or local device. This dimensional shift in where processing occurs eliminates the need for continuous high-bandwidth cloud connectivity while maintaining full data analysis capability, allowing operation in remote areas without internet access
Solution Approach 2:
The system introduces an intermediate processing layer between image capture and final analysis by pre-processing images through color space transformation and feature extraction before anomaly detection. This intermediary processing step reduces data complexity and enables efficient local analysis, reducing dependency on cloud-based processing infrastructure
3Measurement precision
If comprehensive field analysis is performed, then detection accuracy is improved, but turnaround time increases to 8-24 hours
Solution Approach 1:
The patent performs preliminary image processing including color space transformation to HSV or LAB, background subtraction, and feature extraction before anomaly detection. By pre-processing images to enhance relevant features and reduce complexity, the system maintains high detection accuracy while enabling faster processing times, delivering results in minutes rather than hours
Solution Approach 2:
The system segments the comprehensive field analysis into distinct processing stages: initial image capture, color space transformation, anomaly detection, classification, and result generation. This segmentation allows parallel processing of different image regions and optimization of each processing stage independently, significantly reducing overall turnaround time while maintaining detection accuracy
4Area of stationary object
If current monitoring systems provide general field analysis, then broad coverage is achieved, but actionable specificity is lost
Solution Approach 1:
The patent applies different processing and analysis methods to different regions of the field based on local characteristics. Anomaly detection algorithms identify specific problem areas, and classification models provide localized diagnoses tailored to each detected anomaly. This local quality approach ensures that each region receives appropriate analysis depth, maintaining actionable specificity while covering the entire field
Solution Approach 2:
Instead of providing general field analysis and then requiring manual verification, the system inverts the approach by using automated classification to generate specific actionable diagnoses directly. The system doesn't just identify problem locations but also classifies the likely causes (weeds, diseases, pests, nutrient deficiencies), providing actionable information without requiring agronomist verification of each finding
Data Source
AI summary
The present disclosure provides a system for monitoring unstructured environments. A predetermined path can be determined according to an assignment of geolocations to one or more agronomically anomalous target areas, where the one or more agronomically anomalous target areas are determined according to an analysis of a plurality of first images that automatically identifies a target area that deviates from a determination of an average of the plurality of first images that represents an anomalous place within a predetermined area, where the plurality of first images of the predetermined area are captured by a camera during a flight over the predetermined area. A camera of an unmanned vehicle can capture at least one second image of the one or more agronomically anomalous target areas as the unmanned vehicle travels along the predetermined path.